The Evolution of Master Data Management: Connecting the Big Data Dots

The diversity of data besieging organizations with the eruption of big data is manifest most acutely in a gamut of data sources which far outstrips those of yesterday. Harnessing both internal, proprietary sources in tandem with external sources via cloud, social, and machine-generated streaming technologies presents a number of challenges to the enterprise in terms of:

Integration: Aligning different data types, formats, and sources with one another is a requisite for determining how data interrelates—which is one of the chief benefits associated with incorporating multiple sources.

Accessibility: Retrieving, importing and exporting such varieties of data at scale can become extremely time-consuming without adequate measures to hasten the process.

However, there are a number of recent and swiftly impending developments with Master Data Management systems which will give organizations the flexibility, speed, and acuteness to successfully contend with the explosion of data sources which characterizes contemporary data management so that, according to Stibo Systems Senior Vice President of Strategy & Communications Christophe Marcant, they can “be in a position where data is no longer an impediment to the initiative the enterprise wants to embrace. To be in the position where when you have to be fast, when you have to react, when you have to adapt, and be in a position that indeed we’re going to meet that challenge, is definitely much more a driver today than it was before.”

Relationship Awareness
The surfeit of data sources has produced a much greater need to readily determine the relationships between data elements—particularly those between differing sources. Multi-domain MDM hubs are able to implement relationship management between sources and domains so organizations can extract more value from their various types of data. “The ability to connect the dots, what we call the connectedness between different sources of data, that is going to be much more of the trend of what we’re going to see in the future,” Marcant remarked. From an MDM standpoint, the ability to detect the relationships between various domains is imperative. Knowledge gleaned from product domains can impact future customer interactions and reveal how to optimize those interactions to achieve business objectives. Insight between product and supplier domains yields the same sort of benefits, as does that between other combinations of domains. “I think that if we bring many different types of data together and connect them together, then the value is in the relationships,” Marcant reflected.

Integrating Data with Machine Learning
Integrating data from multiple sources in a way which successfully organizes and gives structure to data lakes or the Internet of Things requires a form of data modeling at variance with that in the past. Previously, it was sufficient to “build the data model to store information, and that data model would provide constraints, business rules and the entities the enterprise needed. Then that data model would be shared with business partners in order to access information which was being trusted, validated, and processed appropriately,” Marcant said. However, the bevy of sources and the flexibility required of organizations to make use of them all but obsoletes this paradigm, and places a much greater premium on a more nimble approach to data modeling. The near future will involve machine learning as one of the primary means of implementing such data modeling, in which “you still have a strong, flexible data model tool at the center of the MDM facility but when you consider the integration, you definitely leverage machine learning to collect the data in whatever shape or form it comes in and discover at that point in time how the data from outside relates to that data model you have created,” Marcant said.

Mapping to Data Models
Moreover, machine learning will also play a fundamental role in mapping data to data models which will substantially speed up the on-boarding process for new data into an MDM data model which is characteristic of the enterprise as a whole. The rapidity with which new sources can fit into that modeling is facilitated by the machine learning assisted mapping since, “the more you map data, the faster it goes,” Marcant observed. Thus, machine learning is projected to minimize the sort of manual data preparation which takes so much time in data modeling. However, the models themselves will still rely on taxonomies and classifications in which organizations are able to set their own terms and definitions to create a model which is truly indicative of their particular business needs. These classifications provide a granular degree of descriptions of data elements which machine learning technologies will determine how to map from new or additional data sources.

In-Memory Accessibility
The newfound emphasis on relationships between differing data types and sources enables a nimble approach to structuring, recording, and referencing data which is integral to the velocities associated with big data. Another critical tool which helps MDM facilitate such timely accessibility is in-memory capabilities, which are pivotal for the retrieval of data for various applications. The core of the expedience attributed to in-memory technologies is an elevated productivity in which users are able to accomplish more much quicker than ever before. “Every single facet of the solution goes faster,” Marcant stated. “When you conduct a search, the search is going to be 50 times faster. When you refresh your UI, when you have a very large complex matrix of data that has to be displayed, it comes right away.”

Enduring Centralization
The capital driver for this much more responsive form of MDM which is able to speedily handle a diversity of data sources, their integration and their modeling in time to make use of them today is the numerous forms of big data. Whether manifest as a data lake or in attempts to leverage the IoT, the consistent trust MDM enables of data assets is instrumental in creating success from these endeavors. The need for such a system of reference and record is an enduring one, which a brief look at the past readily corroborates. “There is no reason to believe we would do better today than we were yesterday,” Marcant said. “Why would we believe that the data lake would be more efficient than the data warehouse of yesterday?”

The truth is it won’t—without the aforementioned developments in MDM and in other platforms, which can help to standardize today’s data lakes for systematic, orderly reuse of them.

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